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Electronics
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Biomedical Image Processing and Classification
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Biomedical Image Processing and Classification

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 September 2020) | Viewed by 33837

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editor

Dr. Luca Mesin

E-Mail Website
Guest Editor
Department of Electronics and Telecommunications, Polytechnic University of Turin, Turin, Italy
Interests: biomedical signal and image processing and classification; biophysical modelling; clinical studies; mathematical biology and physiology; noninvasive monitoring of the volemic status of patients; nonlinear biomedical signal processing; optimal non-uniform down-sampling; systems for human–machine interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical image processing is an interdisciplinary field that spreads its foundations throughout a variety of disciplines, including electronic engineering, computer science, physics, mathematics, physiology, and medicine. Several imaging techniques have been developed, providing many approaches to the study of the body, including X-rays for computed tomography, ultrasounds, magnetic resonance, radioactive pharmaceuticals used in nuclear medicine (for positron emission tomography and single-photon emission computed tomography), elastography, functional near-infrared spectroscopy, endoscopy, photoacoustic imaging, and thermography. Even bioelectric sensors, when using high-density systems (e.g., in electroencephalography or electromyography), can provide maps that can be studied with image processing methods. Biomedical image processing is finding an increasing number of important applications, for example, to study the internal structure or function of an organ and in the diagnosis or treatment of a disease. If associated with classification methods, it can support the development of computer-aided diagnosis (CAD) systems, e.g., for the identification of a diseased tissue or a specific lesion or malformation.

The aim of this Special Issue is to collect high-quality works that document a wide range of image processing applications to biomedical problems. Topics of interest include (but are not limited to) image enhancement, registration, segmentation, restoration, compression, and movement tracking, with the aim of identifying tissue properties or the pathology of a patient.

Prof. Dr. Luca Mesin
Guest Editor

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Keywords

  • Image registration
  • Image segmentation
  • Motion tracking
  • Computer-added diagnosis
  • Deep learning
  • Machine learning and classification
  • Patient-specific diagnosis

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Related Special Issue

Published Papers (7 papers)

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Editorial

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4 pages, 164 KiB  
Editorial
Biomedical Image Processing and Classification
by Luca Mesin
Electronics 2021, 10(1), 66; https://doi.org/10.3390/electronics10010066 - 1 Jan 2021
Cited by 2 | Viewed by 2262
Abstract
Biomedical image processing is an interdisciplinary field [...] Full article
(This article belongs to the Special Issue Biomedical Image Processing and Classification)

Research

Jump to: Editorial

14 pages, 1229 KiB  
Article
Automated Volume Status Assessment Using Inferior Vena Cava Pulsatility
by Luca Mesin, Silvestro Roatta, Paolo Pasquero and Massimo Porta
Electronics 2020, 9(10), 1671; https://doi.org/10.3390/electronics9101671 - 13 Oct 2020
Cited by 13 | Viewed by 4527
Abstract
Assessment of volume status is important to correctly plan the treatment of patients admitted and managed by cardiology, emergency and internal medicine departments. Non-invasive assessment of volume status by echography of the inferior vena cava (IVC) is a promising possibility, but its clinical [...] Read more.
Assessment of volume status is important to correctly plan the treatment of patients admitted and managed by cardiology, emergency and internal medicine departments. Non-invasive assessment of volume status by echography of the inferior vena cava (IVC) is a promising possibility, but its clinical use is limited by poor reproducibility of current standard procedures. We have developed new algorithms to extract reliable information from non-invasive IVC monitoring by ultrasound (US) imaging. Both long and short axis US B-mode video-clips were taken from 50 patients, in either hypo-, eu-, or hyper-volemic conditions. The video-clips were processed to extract static and dynamic indexes characterizing the IVC behaviour. Different binary tree models (BTM) were developed to identify patient conditions on the basis of those indexes. The best classifier was a BTM using IVC pulsatility indexes as input features. Its accuracy (78.0% when tested with a leave-one-out approach) is superior to that achieved using indexes measured by the standard clinical method from M-mode US recordings. These results were obtained with patients in conditions of normal respiratory function and cardiac rhythm. Further studies are necessary to extend this approach to patients with more complex cardio-respiratory conditions. Full article
(This article belongs to the Special Issue Biomedical Image Processing and Classification)
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16 pages, 8229 KiB  
Article
Karpinski Score under Digital Investigation: A Fully Automated Segmentation Algorithm to Identify Vascular and Stromal Injury of Donors’ Kidneys
by Massimo Salvi, Alessandro Mogetta, Kristen M. Meiburger, Alessandro Gambella, Luca Molinaro, Antonella Barreca, Mauro Papotti and Filippo Molinari
Electronics 2020, 9(10), 1644; https://doi.org/10.3390/electronics9101644 - 8 Oct 2020
Cited by 20 | Viewed by 4356
Abstract
In kidney transplantations, the evaluation of the vascular structures and stromal areas is crucial for determining kidney acceptance, which is currently based on the pathologist’s visual evaluation. In this context, an accurate assessment of the vascular and stromal injury is fundamental to assessing [...] Read more.
In kidney transplantations, the evaluation of the vascular structures and stromal areas is crucial for determining kidney acceptance, which is currently based on the pathologist’s visual evaluation. In this context, an accurate assessment of the vascular and stromal injury is fundamental to assessing the nephron status. In the present paper, the authors present a fully automated algorithm, called RENFAST (Rapid EvaluatioN of Fibrosis And vesselS Thickness), for the segmentation of kidney blood vessels and fibrosis in histopathological images. The proposed method employs a novel strategy based on deep learning to accurately segment blood vessels, while interstitial fibrosis is assessed using an adaptive stain separation method. The RENFAST algorithm is developed and tested on 350 periodic acid–Schiff (PAS) images for blood vessel segmentation and on 300 Massone’s trichrome (TRIC) stained images for the detection of renal fibrosis. In the TEST set, the algorithm exhibits excellent segmentation performance in both blood vessels (accuracy: 0.8936) and fibrosis (accuracy: 0.9227) and outperforms all the compared methods. To the best of our knowledge, the RENFAST algorithm is the first fully automated method capable of detecting both blood vessels and fibrosis in digital histological images. Being very fast (average computational time 2.91 s), this algorithm paves the way for automated, quantitative, and real-time kidney graft assessments. Full article
(This article belongs to the Special Issue Biomedical Image Processing and Classification)
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12 pages, 3841 KiB  
Article
Deep Learning with Limited Data: Organ Segmentation Performance by U-Net
by Michelle Bardis, Roozbeh Houshyar, Chanon Chantaduly, Alexander Ushinsky, Justin Glavis-Bloom, Madeleine Shaver, Daniel Chow, Edward Uchio and Peter Chang
Electronics 2020, 9(8), 1199; https://doi.org/10.3390/electronics9081199 - 26 Jul 2020
Cited by 26 | Viewed by 6023
Abstract
(1) Background: The effectiveness of deep learning artificial intelligence depends on data availability, often requiring large volumes of data to effectively train an algorithm. However, few studies have explored the minimum number of images needed for optimal algorithmic performance. (2) Methods: This institutional [...] Read more.
(1) Background: The effectiveness of deep learning artificial intelligence depends on data availability, often requiring large volumes of data to effectively train an algorithm. However, few studies have explored the minimum number of images needed for optimal algorithmic performance. (2) Methods: This institutional review board (IRB)-approved retrospective review included patients who received prostate magnetic resonance imaging (MRI) between September 2014 and August 2018 and a magnetic resonance imaging (MRI) fusion transrectal biopsy. T2-weighted images were manually segmented by a board-certified abdominal radiologist. Segmented images were trained on a deep learning network with the following case numbers: 8, 16, 24, 32, 40, 80, 120, 160, 200, 240, 280, and 320. (3) Results: Our deep learning network’s performance was assessed with a Dice score, which measures overlap between the radiologist’s segmentations and deep learning-generated segmentations and ranges from 0 (no overlap) to 1 (perfect overlap). Our algorithm’s Dice score started at 0.424 with 8 cases and improved to 0.858 with 160 cases. After 160 cases, the Dice increased to 0.867 with 320 cases. (4) Conclusions: Our deep learning network for prostate segmentation produced the highest overall Dice score with 320 training cases. Performance improved notably from training sizes of 8 to 120, then plateaued with minimal improvement at training case size above 160. Other studies utilizing comparable network architectures may have similar plateaus, suggesting suitable results may be obtainable with small datasets. Full article
(This article belongs to the Special Issue Biomedical Image Processing and Classification)
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17 pages, 4406 KiB  
Article
Novel Biased Normalized Cuts Approach for the Automatic Segmentation of the Conjunctiva
by Giovanni Dimauro and Lorenzo Simone
Electronics 2020, 9(6), 997; https://doi.org/10.3390/electronics9060997 - 14 Jun 2020
Cited by 19 | Viewed by 3910
Abstract
Anemia is a common public health disease diffused worldwide. In many cases it affects the daily lives of patients needing medical assistance and continuous monitoring. Medical literature states empirical evidence of a correlation between conjunctival pallor on physical examinations and its association with [...] Read more.
Anemia is a common public health disease diffused worldwide. In many cases it affects the daily lives of patients needing medical assistance and continuous monitoring. Medical literature states empirical evidence of a correlation between conjunctival pallor on physical examinations and its association with anemia diagnosis. Although humans exhibit a natural expertise in pattern recognition and associative skills based on hue properties, the variance of estimates is high, requiring blood sampling even for monitoring. To design automatic systems for the objective evaluation of pallor utilizing digital images of the conjunctiva, it is necessary to obtain reliable automatic segmentation of the eyelid conjunctiva. In this study, we propose a graph partitioning segmentation approach. The semantic segmentation procedure of a diagnostically meaningful region of interest has been proposed for exploiting normalized cuts for perceptual grouping, thereby introducing a bias towards spectrophotometry features of hemoglobin. The reliability of the identification of the region of interest is demonstrated both with standard metrics and by measuring the correlation between the color of the ROI and the hemoglobin level based on 94 samples distributed in relation to age, sex and hemoglobin concentration. The region of interest automatically segmented is suitable for diagnostic procedures based on quantitative hemoglobin estimation of exposed tissues of the conjunctiva. Full article
(This article belongs to the Special Issue Biomedical Image Processing and Classification)
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15 pages, 8120 KiB  
Article
Semantic Segmentation Framework for Glomeruli Detection and Classification in Kidney Histological Sections
by Nicola Altini, Giacomo Donato Cascarano, Antonio Brunetti, Francescomaria Marino, Maria Teresa Rocchetti, Silvia Matino, Umberto Venere, Michele Rossini, Francesco Pesce, Loreto Gesualdo and Vitoantonio Bevilacqua
Electronics 2020, 9(3), 503; https://doi.org/10.3390/electronics9030503 - 19 Mar 2020
Cited by 49 | Viewed by 7956
Abstract
The evaluation of kidney biopsies performed by expert pathologists is a crucial process for assessing if a kidney is eligible for transplantation. In this evaluation process, an important step consists of the quantification of global glomerulosclerosis, which is the ratio between sclerotic glomeruli [...] Read more.
The evaluation of kidney biopsies performed by expert pathologists is a crucial process for assessing if a kidney is eligible for transplantation. In this evaluation process, an important step consists of the quantification of global glomerulosclerosis, which is the ratio between sclerotic glomeruli and the overall number of glomeruli. Since there is a shortage of organs available for transplantation, a quick and accurate assessment of global glomerulosclerosis is essential for retaining the largest number of eligible kidneys. In the present paper, the authors introduce a Computer-Aided Diagnosis (CAD) system to assess global glomerulosclerosis. The proposed tool is based on Convolutional Neural Networks (CNNs). In particular, the authors considered approaches based on Semantic Segmentation networks, such as SegNet and DeepLab v3+. The dataset has been provided by the Department of Emergency and Organ Transplantations (DETO) of Bari University Hospital, and it is composed of 26 kidney biopsies coming from 19 donors. The dataset contains 2344 non-sclerotic glomeruli and 428 sclerotic glomeruli. The proposed model consents to achieve promising results in the task of automatically detecting and classifying glomeruli, thus easing the burden of pathologists. We get high performance both at pixel-level, achieving mean F-score higher than 0.81, and Weighted Intersection over Union (IoU) higher than 0.97 for both SegNet and Deeplab v3+ approaches, and at object detection level, achieving 0.924 as best F-score for non-sclerotic glomeruli and 0.730 as best F-score for sclerotic glomeruli. Full article
(This article belongs to the Special Issue Biomedical Image Processing and Classification)
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23 pages, 5338 KiB  
Article
An Efficient Hybrid Fuzzy-Clustering Driven 3D-Modeling of Magnetic Resonance Imagery for Enhanced Brain Tumor Diagnosis
by Suresh Kanniappan, Duraimurugan Samiayya, Durai Raj Vincent P M, Kathiravan Srinivasan, Dushantha Nalin K. Jayakody, Daniel Gutiérrez Reina and Atsushi Inoue
Electronics 2020, 9(3), 475; https://doi.org/10.3390/electronics9030475 - 12 Mar 2020
Cited by 15 | Viewed by 3366
Abstract
Brain tumor detection and its analysis are essential in medical diagnosis. The proposed work focuses on segmenting abnormality of axial brain MR DICOM slices, as this format holds the advantage of conserving extensive metadata. The axial slices presume the left and right part [...] Read more.
Brain tumor detection and its analysis are essential in medical diagnosis. The proposed work focuses on segmenting abnormality of axial brain MR DICOM slices, as this format holds the advantage of conserving extensive metadata. The axial slices presume the left and right part of the brain is symmetric by a Line of Symmetry (LOS). A semi-automated system is designed to mine normal and abnormal structures from each brain MR slice in a DICOM study. In this work, Fuzzy clustering (FC) is applied to the DICOM slices to extract various clusters for different k. Then, the best-segmented image that has high inter-class rigidity is obtained using the silhouette fitness function. The clustered boundaries of the tissue classes further enhanced by morphological operations. The FC technique is hybridized with the standard image post-processing techniques such as marker controlled watershed segmentation (MCW), region growing (RG), and distance regularized level sets (DRLS). This procedure is implemented on renowned BRATS challenge dataset of different modalities and a clinical dataset containing axial T2 weighted MR images of a patient. The sequential analysis of the slices is performed using the metadata information present in the DICOM header. The validation of the segmentation procedures against the ground truth images authorizes that the segmented objects of DRLS through FC enhanced brain images attain maximum scores of Jaccard and Dice similarity coefficients. The average Jaccard and dice scores for segmenting tumor part for ten patient studies of the BRATS dataset are 0.79 and 0.88, also for the clinical study 0.78 and 0.86, respectively. Finally, 3D visualization and tumor volume estimation are done using accessible DICOM information. Full article
(This article belongs to the Special Issue Biomedical Image Processing and Classification)
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